Stress x EE MA
01/07/2021
Loading packages
knitr::opts_chunk$set(echo = TRUE)
pacman::p_load(tidyverse,
here,
metafor,
emmeans,
orchaRd)Loading data and functions
dat <- read_csv(here("Data","Pilot_data.csv"))
# Load custom function to extract data
source(here("R/functions.R")) Data organisation
Getting effect sizes from function, ‘flipping’ effect sizes so that all effect sizes are higher values = individuals do better and learning/memory, and shifting negative values to possitive as lnRR cannot use negative values
#fixing negative values in study 16 (this doesn't work as can't use 0 in lnRR calculation)
#modifying study 16 that has negative values: shifting everything up by the lowest value
#note, this results in inf lnRR - is it because of the zeros?
#dat1 <- dat %>%
# rowwise() %>%
#mutate(min.mean = min(CC_mean, EC_mean, CS_mean, ES_mean)) %>%
#ungroup() %>%
#mutate(CC_mean = case_when(First_author == "Wang" ~ CC_mean + abs(min.mean),
# TRUE ~ as.numeric(.$CC_mean))) %>%
#mutate(EC_mean = case_when(First_author == "Wang" ~ EC_mean + abs(min.mean),
#TRUE ~ as.numeric(.$EC_mean))) %>%
#mutate(CS_mean = case_when(First_author == "Wang" ~ CS_mean + abs(min.mean),
#TRUE ~ as.numeric(.$CS_mean))) %>%
#mutate(ES_mean = case_when(First_author == "Wang" ~ ES_mean + abs(min.mean),
#TRUE ~ as.numeric(.$ES_mean)))
#Getting effect sizes
effect_size <- effect_set(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
data = dat)
#Removing missing effect sizes
full_info <- which(complete.cases(effect_size) == TRUE)
dat_effect <- cbind(dat, effect_size)
dat <- dat_effect[full_info, ]
#Flipping 'lower is better' effect sizes
#flipping lnRR for values where higher = worse
dat$lnRR_Ea <- ifelse(dat$Response_direction == 2, dat$lnRR_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E)) # currently NAswhich causes error
dat$lnRR_Sa <- ifelse(dat$Response_direction == 2, dat$lnRR_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S)) # currently NAswhich causes error
dat$lnRR_ESa <- ifelse(dat$Response_direction == 2, dat$lnRR_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES)) # currently NAswhich causes error
#flipping SMD
dat$SMD_Ea <- ifelse(dat$Response_direction == 2, dat$SMD_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_E)) # currently NAswhich causes error
dat$SMD_Sa <- ifelse(dat$Response_direction == 2, dat$SMD_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_S)) # currently NAswhich causes error
dat$SMD_ESa <- ifelse(dat$Response_direction == 2, dat$SMD_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_ES))Assigning human readable words to moderators
dat <- dat %>% mutate(Type_learning = case_when(Type_learning == 1 ~ "Habituation",
Type_learning == 2 ~ "Conditioning",
Type_learning == 3 ~ "Recognition",
Type_learning == 4 ~ "Unclear"),
Learning_vs_memory = case_when(Learning_vs_memory == 1 ~ "Learning",
Learning_vs_memory == 2 ~ "Memory",
Learning_vs_memory == 1 ~ "Unclear"),
Appetitive_vs_aversive = case_when(Appetitive_vs_aversive == 1 ~"Appetitive",
Appetitive_vs_aversive == 2 ~ "Aversive",
Appetitive_vs_aversive == 3 ~ "Not applicable",
Appetitive_vs_aversive == 4 ~ "Unclear"),
Type_stress_exposure = case_when(Type_stress_exposure == 1 ~ "Density",
Type_stress_exposure == 2 ~ "Scent",
Type_stress_exposure == 3 ~ "Shock",
Type_stress_exposure == 4 ~ "Exertion",
Type_stress_exposure == 5 ~ "Restraint",
Type_stress_exposure == 6 ~ "MS",
Type_stress_exposure == 7 ~ "Circadian rhythm",
Type_stress_exposure == 8 ~ "Noise",
Type_stress_exposure == 9 ~ "Other",
Type_stress_exposure == 10 ~ "Combination",
Type_stress_exposure == 11 ~ "unclear"),
Age_stress_exposure = case_when(Age_stress_exposure == 1 ~ "Prenatal",
Age_stress_exposure == 2 ~ "Juvenile",
Age_stress_exposure == 3 ~ "Adult",
Age_stress_exposure == 4 ~ "Unclear"),
Stress_duration = case_when(Stress_duration == 1 ~ "Acute",
Stress_duration == 2 ~ "Chronic",
Stress_duration == 3 ~ "Intermittent",
Stress_duration == 4 ~ "Unclear"),
EE_social = case_when(EE_social == 1 ~ "Social",
EE_social== 2 ~ "Non-social",
EE_social == 3 ~ "Unclear"),
EE_exercise = case_when(EE_exercise == 1 ~ "Exercise",
EE_exercise == 2 ~ "No exercise"),
Age_EE_exposure = case_when(Age_EE_exposure == 1 ~ "Prenatal",
Age_EE_exposure == 2 ~ "Juvenile",
Age_EE_exposure == 3 ~ "Adult",
Age_EE_exposure == 4 ~ "Unclear"))Modelling with lnRR
Things that remain to be done: - Incorporate strain as random effect - Consider including VCV - Check study 82 as it seems to be an ourlier
Stress
Intercept model
Learning and memory are significantly reduced due to stress. High heterogeneity
mod_S0 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S0) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1.8202 3.6404 9.6404 16.8605 9.9481
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0266 0.1632 27 no Study_ID
## sigma^2.2 0.0084 0.0914 83 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 82) = 485.4512, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.0928 0.0367 -2.5292 82 0.0133 -0.1658 -0.0198 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S0) ## I2_total I2_Study_ID I2_ES_ID
## 0.9126441 0.6947688 0.2178753funnel(mod_S0)
orchard_plot(mod_S0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
dat$Type_learning<-as.factor(dat$Type_learning)
mod_S1 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S1)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1.5005 3.0010 13.0010 24.9112 13.8118
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0296 0.1721 27 no Study_ID
## sigma^2.2 0.0078 0.0881 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 483.5512, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 3.2077, p-val = 0.0275
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning -0.0991 0.0405 -2.4474 80 0.0166 -0.1797
## Type_learningHabituation -0.2266 0.0887 -2.5543 80 0.0125 -0.4031
## Type_learningRecognition -0.0356 0.0575 -0.6184 80 0.5381 -0.1500
## ci.ub
## Type_learningConditioning -0.0185 *
## Type_learningHabituation -0.0501 *
## Type_learningRecognition 0.0789
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S1) ## R2_marginal R2_coditional
## 0.04543202 0.80182374# Orchard plot
orchard_plot(mod_S1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_S2 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID, ~1|ES_ID),
test = "t",
data = dat)
summary(mod_S2) ##
## Multivariate Meta-Analysis Model (k = 77; method: REML)
##
## logLik Deviance AIC BIC AICc
## -4.9861 9.9722 17.9722 27.2422 18.5437
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0316 0.1778 27 no Study_ID
## sigma^2.2 0.0079 0.0889 77 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 477.8435, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 75) = 2.9032, p-val = 0.0610
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning -0.0558 0.0494 -1.1301 75 0.2620 -0.1541
## Learning_vs_memoryMemory -0.0975 0.0410 -2.3757 75 0.0201 -0.1792
## ci.ub
## Learning_vs_memoryLearning 0.0426
## Learning_vs_memoryMemory -0.0157 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S2) ## R2_marginal R2_coditional
## 0.00967603 0.80203493# Orchard plot
orchard_plot(mod_S2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_S3 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID, ~1|ES_ID),
test = "t",
data = dat)
summary(mod_S3)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -2.1549 4.3099 14.3099 26.2200 15.1207
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0255 0.1596 27 no Study_ID
## sigma^2.2 0.0097 0.0983 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 407.5047, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 2.6321, p-val = 0.0556
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive -0.1818 0.0886 -2.0507 80 0.0436
## Appetitive_vs_aversiveAversive -0.0834 0.0429 -1.9429 80 0.0555
## Appetitive_vs_aversiveNot applicable -0.0680 0.0503 -1.3527 80 0.1800
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive -0.3581 -0.0054 *
## Appetitive_vs_aversiveAversive -0.1688 0.0020 .
## Appetitive_vs_aversiveNot applicable -0.1681 0.0320
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S3) ## R2_marginal R2_coditional
## 0.04201061 0.73658351# Orchard plot
orchard_plot(mod_S3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Type of stress
The type of stress manipulation
mod_S4 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S4) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -2.2721 4.5442 20.5442 39.2946 22.6618
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0324 0.1801 27 no Study_ID
## sigma^2.2 0.0090 0.0950 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 77) = 391.0125, p-val < .0001
##
## Test of Moderators (coefficients 1:6):
## F(df1 = 6, df2 = 77) = 1.1700, p-val = 0.3312
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination -0.0484 0.1186 -0.4080 77 0.6844 -0.2846
## Type_stress_exposureMS -0.0597 0.0617 -0.9677 77 0.3362 -0.1827
## Type_stress_exposureNoise -0.0786 0.1120 -0.7024 77 0.4846 -0.3016
## Type_stress_exposureOther -0.1540 0.2512 -0.6132 77 0.5416 -0.6542
## Type_stress_exposureRestraint -0.1738 0.0793 -2.1907 77 0.0315 -0.3318
## Type_stress_exposureShock -0.0742 0.1490 -0.4983 77 0.6197 -0.3709
## ci.ub
## Type_stress_exposureCombination 0.1878
## Type_stress_exposureMS 0.0632
## Type_stress_exposureNoise 0.1443
## Type_stress_exposureOther 0.3462
## Type_stress_exposureRestraint -0.0158 *
## Type_stress_exposureShock 0.2224
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S4)## R2_marginal R2_coditional
## 0.0442837 0.7921150# Orchard plot
orchard_plot(mod_S4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of stress
The age at which the individuals were exposed to the stressor. Note there are a lot of ‘unkown’ age as authors only report PND which needs to be researched. I’m wondering if this also needs an ‘adolescence’ category as this seesm to be popular in rodent research
mod_S5 <-rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S5) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -0.5590 1.1179 13.1179 27.3346 14.2846
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0254 0.1594 27 no Study_ID
## sigma^2.2 0.0086 0.0929 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 79) = 348.3546, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 79) = 2.8005, p-val = 0.0314
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Age_stress_exposureAdult -0.1029 0.0938 -1.0972 79 0.2759 -0.2897
## Age_stress_exposureJuvenile -0.0331 0.0478 -0.6932 79 0.4902 -0.1283
## Age_stress_exposurePrenatal -0.1828 0.1030 -1.7746 79 0.0798 -0.3878
## Age_stress_exposureUnclear -0.2284 0.0905 -2.5236 79 0.0136 -0.4085
## ci.ub
## Age_stress_exposureAdult 0.0838
## Age_stress_exposureJuvenile 0.0620
## Age_stress_exposurePrenatal 0.0222 .
## Age_stress_exposureUnclear -0.0482 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S5) ## R2_marginal R2_coditional
## 0.1348675 0.7806227# Orchard plot
orchard_plot(mod_S5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Acute vs chronic stress
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2
mod_S6 <-rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S6) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -0.2670 0.5339 12.5339 26.7506 13.7006
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0239 0.1546 27 no Study_ID
## sigma^2.2 0.0092 0.0959 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 79) = 466.3131, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 79) = 3.1070, p-val = 0.0199
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Stress_durationAcute 0.0729 0.0827 0.8818 79 0.3805 -0.0917
## Stress_durationChronic -0.1297 0.0422 -3.0725 79 0.0029 -0.2138
## Stress_durationIntermittent -0.2395 0.1732 -1.3826 79 0.1707 -0.5843
## Stress_durationUnclear -0.0733 0.1342 -0.5464 79 0.5864 -0.3405
## ci.ub
## Stress_durationAcute 0.2375
## Stress_durationChronic -0.0457 **
## Stress_durationIntermittent 0.1053
## Stress_durationUnclear 0.1938
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S6) ## R2_marginal R2_coditional
## 0.1847287 0.7733348# Orchard plot
orchard_plot(mod_S6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Environmental enrichment
Intercept model
Learning and memory are significantly improved when housed with environmnetal enrichment
mod_E0 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E0) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.5805 19.1609 25.1609 32.3811 25.4686
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0139 0.1177 27 no Study_ID
## sigma^2.2 0.0274 0.1655 83 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 82) = 633.2567, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1864 0.0341 5.4634 82 <.0001 0.1185 0.2542 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E0) ## I2_total I2_Study_ID I2_ES_ID
## 0.9248889 0.3106993 0.6141896funnel(mod_E0)
#trying orchard plot
orchard_plot(mod_E0, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
mod_E1 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E1)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.1741 14.3482 24.3482 36.2584 25.1591
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0166 0.1289 27 no Study_ID
## sigma^2.2 0.0234 0.1530 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 627.5479, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 11.9133, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning 0.2281 0.0384 5.9465 80 <.0001 0.1517
## Type_learningHabituation 0.0484 0.1008 0.4808 80 0.6320 -0.1521
## Type_learningRecognition 0.0784 0.0649 1.2082 80 0.2305 -0.0507
## ci.ub
## Type_learningConditioning 0.3044 ***
## Type_learningHabituation 0.2490
## Type_learningRecognition 0.2075
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E1) ## R2_marginal R2_coditional
## 0.09962679 0.47342831# Orchard plot
orchard_plot(mod_E1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_E2 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E2) ##
## Multivariate Meta-Analysis Model (k = 77; method: REML)
##
## logLik Deviance AIC BIC AICc
## -8.0101 16.0203 24.0203 33.2902 24.5917
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0189 0.1374 27 no Study_ID
## sigma^2.2 0.0202 0.1422 77 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 576.4243, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 75) = 14.6261, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.2043 0.0515 3.9645 75 0.0002 0.1016
## Learning_vs_memoryMemory 0.1905 0.0389 4.8910 75 <.0001 0.1129
## ci.ub
## Learning_vs_memoryLearning 0.3070 ***
## Learning_vs_memoryMemory 0.2680 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E2) ## R2_marginal R2_coditional
## 0.001087355 0.483536420# Orchard plot
orchard_plot(mod_E2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_E3 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E3)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.5510 13.1020 23.1020 35.0121 23.9128
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0173 0.1313 27 no Study_ID
## sigma^2.2 0.0228 0.1510 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 371.0663, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 12.0223, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive 0.1981 0.0863 2.2947 80 0.0244
## Appetitive_vs_aversiveAversive 0.2397 0.0430 5.5697 80 <.0001
## Appetitive_vs_aversiveNot applicable 0.0715 0.0544 1.3139 80 0.1926
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive 0.0263 0.3700 *
## Appetitive_vs_aversiveAversive 0.1540 0.3253 ***
## Appetitive_vs_aversiveNot applicable -0.0368 0.1798
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E3) ## R2_marginal R2_coditional
## 0.1091672 0.4928642# Orchard plot
orchard_plot(mod_E3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_E5<- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E5)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.8006 19.6012 27.6012 37.1790 28.1275
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0152 0.1231 27 no Study_ID
## sigma^2.2 0.0275 0.1658 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 81) = 603.9343, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 81) = 14.2977, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.1839 0.0421 4.3702 81 <.0001 0.1002 0.2677
## EE_exerciseNo exercise 0.1925 0.0625 3.0817 81 0.0028 0.0682 0.3168
##
## EE_exerciseExercise ***
## EE_exerciseNo exercise **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E5) ## R2_marginal R2_coditional
## 0.0003839652 0.3554805192# Orchard plot
orchard_plot(mod_E5, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
mod_E6 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E6) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -8.0766 16.1533 26.1533 38.0634 26.9641
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0124 0.1114 27 no Study_ID
## sigma^2.2 0.0282 0.1680 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 589.9641, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 11.7375, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Age_EE_exposureAdult 0.1577 0.0695 2.2702 80 0.0259 0.0195 0.2959
## Age_EE_exposureJuvenile 0.0075 0.1024 0.0733 80 0.9417 -0.1963 0.2113
## Age_EE_exposureUnclear 0.2251 0.0411 5.4821 80 <.0001 0.1434 0.3068
##
## Age_EE_exposureAdult *
## Age_EE_exposureJuvenile
## Age_EE_exposureUnclear ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E6) ## R2_marginal R2_coditional
## 0.1038652 0.3774706# Orchard plot
orchard_plot(mod_E6, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Interaction of stress and EE
Intercept
Enriched and stress animals are better at learning and memory. TODO: It looks like there is a large but low precision outlier. Should potentially remove?
mod_ES0 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES0) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.6684 85.3368 91.3368 98.5570 91.6445
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0490 0.2214 27 no Study_ID
## sigma^2.2 0.0187 0.1369 83 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 82) = 283.5814, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1642 0.0535 3.0697 82 0.0029 0.0578 0.2706 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_ES0) ## I2_total I2_Study_ID I2_ES_ID
## 0.8349106 0.6040483 0.2308623funnel(mod_ES0)
orchard_plot(mod_ES0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
mod_ES1 <- rma.mv(yi = lnRR_ESa, V = lnRRV_E, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES1)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -60.7036 121.4071 131.4071 143.3173 132.2179
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0932 0.3053 27 no Study_ID
## sigma^2.2 0.1024 0.3199 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 1129.4679, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 3.9660, p-val = 0.0109
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning 0.2587 0.0785 3.2940 80 0.0015 0.1024
## Type_learningHabituation 0.2573 0.1933 1.3311 80 0.1869 -0.1274
## Type_learningRecognition -0.0026 0.1321 -0.0197 80 0.9844 -0.2656
## ci.ub
## Type_learningConditioning 0.4150 **
## Type_learningHabituation 0.6420
## Type_learningRecognition 0.2604
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES1) ## R2_marginal R2_coditional
## 0.04183842 0.49847406# Orchard plot
orchard_plot(mod_ES1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_ES2 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES2) ##
## Multivariate Meta-Analysis Model (k = 77; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.0311 86.0621 94.0621 103.3321 94.6335
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0440 0.2097 27 no Study_ID
## sigma^2.2 0.0191 0.1383 77 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 276.0760, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 75) = 6.4436, p-val = 0.0026
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.2545 0.0735 3.4617 75 0.0009 0.1081
## Learning_vs_memoryMemory 0.1345 0.0555 2.4209 75 0.0179 0.0238
## ci.ub
## Learning_vs_memoryLearning 0.4010 ***
## Learning_vs_memoryMemory 0.2451 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES2) ## R2_marginal R2_coditional
## 0.04831121 0.71161934# Orchard plot
orchard_plot(mod_ES2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_ES3 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES3)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.5999 79.1999 89.1999 101.1100 90.0107
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0526 0.2294 27 no Study_ID
## sigma^2.2 0.0110 0.1048 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 240.2986, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 5.4389, p-val = 0.0019
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive 0.1787 0.1355 1.3193 80 0.1908
## Appetitive_vs_aversiveAversive 0.2331 0.0640 3.6425 80 0.0005
## Appetitive_vs_aversiveNot applicable 0.0309 0.0736 0.4201 80 0.6756
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive -0.0909 0.4483
## Appetitive_vs_aversiveAversive 0.1057 0.3604 ***
## Appetitive_vs_aversiveNot applicable -0.1156 0.1774
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES3) ## R2_marginal R2_coditional
## 0.1001471 0.8447470# Orchard plot
orchard_plot(mod_ES3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Type of stress
The type of stress manipulation
mod_ES4 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES4)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.7572 81.5145 97.5145 116.2649 99.6321
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0667 0.2583 27 no Study_ID
## sigma^2.2 0.0199 0.1409 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 77) = 256.3426, p-val < .0001
##
## Test of Moderators (coefficients 1:6):
## F(df1 = 6, df2 = 77) = 1.6374, p-val = 0.1482
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination 0.0609 0.1779 0.3423 77 0.7330 -0.2933
## Type_stress_exposureMS 0.2008 0.0928 2.1640 77 0.0336 0.0160
## Type_stress_exposureNoise 0.1803 0.1658 1.0877 77 0.2801 -0.1498
## Type_stress_exposureOther 0.6719 0.4186 1.6053 77 0.1125 -0.1616
## Type_stress_exposureRestraint 0.1069 0.1204 0.8876 77 0.3775 -0.1329
## Type_stress_exposureShock 0.1530 0.2216 0.6907 77 0.4919 -0.2882
## ci.ub
## Type_stress_exposureCombination 0.4151
## Type_stress_exposureMS 0.3855 *
## Type_stress_exposureNoise 0.5104
## Type_stress_exposureOther 1.5054
## Type_stress_exposureRestraint 0.3466
## Type_stress_exposureShock 0.5943
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES4)## R2_marginal R2_coditional
## 0.08721812 0.79056434# Orchard plot
orchard_plot(mod_ES4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of stress
The age at which the individuals were exposed to the stressor. Note there are a lot of ‘unkown’ age as authors only report PND which needs to be researched. I’m wondering if this also needs an ‘adolescence’ category as this seesm to be popular in rodent research
mod_ES5 <-rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES5) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -38.2467 76.4933 88.4933 102.7100 89.6600
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0338 0.1838 27 no Study_ID
## sigma^2.2 0.0172 0.1312 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 79) = 210.5035, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 79) = 5.4753, p-val = 0.0006
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Age_stress_exposureAdult -0.0393 0.1213 -0.3236 79 0.7471 -0.2807
## Age_stress_exposureJuvenile 0.1092 0.0619 1.7636 79 0.0817 -0.0141
## Age_stress_exposurePrenatal 0.4045 0.1294 3.1268 79 0.0025 0.1470
## Age_stress_exposureUnclear 0.3755 0.1258 2.9849 79 0.0038 0.1251
## ci.ub
## Age_stress_exposureAdult 0.2022
## Age_stress_exposureJuvenile 0.2325 .
## Age_stress_exposurePrenatal 0.6619 **
## Age_stress_exposureUnclear 0.6260 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES5) ## R2_marginal R2_coditional
## 0.2466401 0.7457772# Orchard plot
orchard_plot(mod_ES5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Acute vs chronic stress
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2 (currentl nearly 43%) - need to redo without outlier (study 82)
mod_ES6 <-rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES6) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -35.5823 71.1646 83.1646 97.3813 84.3313
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0247 0.1572 27 no Study_ID
## sigma^2.2 0.0203 0.1423 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 79) = 244.5309, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 79) = 7.8092, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Stress_durationAcute -0.1655 0.1039 -1.5926 79 0.1152 -0.3723
## Stress_durationChronic 0.2160 0.0516 4.1879 79 <.0001 0.1133
## Stress_durationIntermittent 0.6672 0.2069 3.2245 79 0.0018 0.2553
## Stress_durationUnclear 0.1469 0.1681 0.8740 79 0.3848 -0.1876
## ci.ub
## Stress_durationAcute 0.0413
## Stress_durationChronic 0.3186 ***
## Stress_durationIntermittent 1.0790 **
## Stress_durationUnclear 0.4815
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES6) ## R2_marginal R2_coditional
## 0.4280127 0.7423656# Orchard plot
orchard_plot(mod_ES6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Social enrichment
Does EE also include a manipulation of social environment (i.e., number of individuals in EE relative to control)?
mod_ES7<- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~EE_social-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES7)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.1441 84.2882 92.2882 101.8660 92.8145
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0508 0.2254 27 no Study_ID
## sigma^2.2 0.0193 0.1389 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 81) = 282.4479, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 81) = 4.9205, p-val = 0.0096
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_socialNon-social 0.1172 0.0799 1.4663 81 0.1464 -0.0418 0.2763
## EE_socialSocial 0.2048 0.0738 2.7733 81 0.0069 0.0579 0.3517 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES7) ## R2_marginal R2_coditional
## 0.02631323 0.73195773# Orchard plot
orchard_plot(mod_ES7, mod = "EE_social", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_ES8<- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES8)##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.2887 84.5773 92.5773 102.1551 93.1036
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0503 0.2242 27 no Study_ID
## sigma^2.2 0.0191 0.1382 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 81) = 256.9350, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 81) = 4.7723, p-val = 0.0110
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.1458 0.0642 2.2698 81 0.0259 0.0180 0.2736 *
## EE_exerciseNo exercise 0.2092 0.0998 2.0959 81 0.0392 0.0106 0.4079 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES8) ## R2_marginal R2_coditional
## 0.01272481 0.72808591# Orchard plot
orchard_plot(mod_ES8, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
mod_ES9 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES9) ##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.3423 82.6845 92.6845 104.5946 93.4953
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0505 0.2247 27 no Study_ID
## sigma^2.2 0.0196 0.1399 83 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 80) = 277.0286, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 80) = 3.6708, p-val = 0.0156
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Age_EE_exposureAdult 0.1192 0.1108 1.0758 80 0.2853 -0.1013 0.3397
## Age_EE_exposureJuvenile -0.0190 0.1699 -0.1121 80 0.9111 -0.3572 0.3191
## Age_EE_exposureUnclear 0.2093 0.0667 3.1373 80 0.0024 0.0765 0.3421
##
## Age_EE_exposureAdult
## Age_EE_exposureJuvenile
## Age_EE_exposureUnclear **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES9) ## R2_marginal R2_coditional
## 0.07306424 0.74108911# Orchard plot
orchard_plot(mod_ES9, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Social enrichment
Does EE also include a manipulation of social environment? Note that we excluded any studies that exclusively used social enrichment.s